Students build a formal understanding of probability, considering complex events such as unions, intersections, and complements as well as the concept of independence and conditional probability.  The idea of using a smooth curve to model a data distribution is introduced along with using tables and technology to find areas under a normal curve.  Students make inferences and justify conclusions from sample surveys, experiments, and observational studies.  Data is used from random samples to estimate a population mean or proportion.  Students calculate margin of error and interpret it in context.  Given data from a statistical experiment, students use simulation to create a randomization distribution and use it to determine if there is a significant difference between two treatments.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.

(Nota: Esta es una traducción de un recurso educativo abierto creado por el Departamento de Educación del Estado de Nueva York (NYSED) como parte del proyecto "EngageNY" en 2013. Aunque el recurso real fue traducido por personas, la siguiente descripción se tradujo del inglés original usando Google Translate para ayudar a los usuarios potenciales a decidir si se adapta a sus necesidades y puede contener errores gramaticales o lingüísticos. La descripción original en inglés también se proporciona a continuación.)

Los estudiantes crean una comprensión formal de la probabilidad, considerando eventos complejos como sindicatos, intersecciones y complementos, así como el concepto de independencia y probabilidad condicional. La idea de usar una curva suave para modelar una distribución de datos se introduce junto con el uso de tablas y tecnología para encontrar áreas bajo una curva normal. Los estudiantes hacen inferencias y justifican conclusiones de encuestas de muestra, experimentos y estudios de observación. Los datos se usan de muestras aleatorias para estimar una media o proporción de población. Los estudiantes calculan el margen de error y lo interpretan en contexto. Dados los datos de un experimento estadístico, los estudiantes usan la simulación para crear una distribución de aleatorización y lo usan para determinar si hay una diferencia significativa entre dos tratamientos.

Encuentre el resto de los recursos matemáticos de Engageny en https://archive.org/details/engageny-mathematics.

English Description:
Students build a formal understanding of probability, considering complex events such as unions, intersections, and complements as well as the concept of independence and conditional probability.  The idea of using a smooth curve to model a data distribution is introduced along with using tables and technology to find areas under a normal curve.  Students make inferences and justify conclusions from sample surveys, experiments, and observational studies.  Data is used from random samples to estimate a population mean or proportion.  Students calculate margin of error and interpret it in context.  Given data from a statistical experiment, students use simulation to create a randomization distribution and use it to determine if there is a significant difference between two treatments.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.

Applied Finite Mathematics covers topics including linear equations, matrices, linear programming, the mathematics of finance, sets and counting, probability, Markov chains, and game theory. Endorsed by CollegeOpenTextbooks.org.

CK-12 Foundation gives access to high quality educational materials for K-12 students all over the world.
They have high-quality, standards-aligned, open content in the STEM subjects.

This is an OUTSTANDING resource. Be sure to check out the Math & Science FlexLets

Students - select "switch to student version" from the top menu.

With a focus on experiential learning, Brock University students worked collaboratively with Niagara Catholic District School Board teachers to develop engaging, coding-based activities for students that were explicitly or implicitly about mathematics. For more information, read the News Article from Brock University.

Teams first explored some resources developed by Dr. George Gadanidis, which led them to further develop 4 CT-based math tasks, which summaries you can access below.

1. Probability & Scratch
2. Exploring Geometry & Patterning with Coding
3. Creating a T-Shaped Puzzle with CNC
4. DASHing the way to Scratch

This lesson combines conditional probability and combinations to determine the probability of picking a fair coin given that it flipped 4 out of 6 heads. [Probability playlist: Lesson 16 of 29]

You can find statistics in every day life - this example explains how to calculate the chance of a member of a committee is receiving a bribe. This resource is from PUMAS - Practical Uses of Math and Science - a collection of brief examples created by scientists and engineers showing how math and science topics taught in K-12 classes have real world applications.

These games support student development of number sense and the concept of mathematical operations

Using a website simulation tool, students build on their understanding of random processes on networks to interact with the graph of a social network of individuals and simulate the spread of a disease. They decide which two individuals on the network are the best to vaccinate in an attempt to minimize the number of people infected and "curb the epidemic." Since the results are random, they run multiple simulations and compute the average number of infected individuals before analyzing the results and assessing the effectiveness of their vaccination strategies.

The purpose of this lesson is to introduce the unit on statistics and probability.

Included is a YouTube video to support Grade 5 Blended Learning Math - Unit 7.0: Statistics and Probability - Introduction.

The purpose of this lesson is to introduce the grade 6 math unit on data analysis and probability.

Included is a YouTube video to support Grade 6 Blended Learning Math - Unit 7.0: Data Analysis and Probability - Introduction.

The purpose of this lesson is to explore theoretical probability.

Included is a YouTube video to support Grade 6 Blended Learning Math - Unit 7.6: Data Analysis and Probability - Theoretical Probability.

The purpose of this lesson is to learn how to express probabilities of events as ratios, fractions and percents.

Included is a YouTube video to support Grade 7 Blended Learning Math - Unit 7.5: Data Analysis - Different Ways to Express Probability.

The purpose of this lesson is to learn how to calculate the probability of multiple independent events.

Included is a YouTube video to support Grade 7 Blended Learning Math - Unit 7.6: Data Analysis - Tree Diagrams.

The purpose of this lesson is to apply what was learned about calculating the probability of independent events.

Included is a YouTube video to support Grade 8 Blended Learning Math - Unit 7.4: Data Analysis and Probability - Solving Problems Involving Independent Events.

Using two different coins and recording the results of both coins helps students dispel this initial misconception as they analyze the graph results. Class discussion should focus on analyzing the data to determine if the game is fair or not. Directions and gameboard are included in the download.

These activities support students as they conceptually develop a sense of how probability affects the outcome of games. Students will find that applying their knowledge of probability will help them win some of the games

Simulate the original experiment that proved that electrons can behave as waves. Watch electrons diffract off a crystal of atoms, interfering with themselves to create peaks and troughs of probability.

These activities help students use organized lists and systematic counting to solve combination problems. Map coloring and networks are also discrete math problems that students can relate to real-world applications.

Explore tunneling splitting in double well potentials. This classic problem describes many physical systems, including covalent bonds, Josephson junctions, and two-state systems such as spin 1/2 particles and ammonia molecules.